CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 33:
Images
Drawing Images
Homework
Look carefully at the Memory and
MemoryButton classes for guidance
Be creative with the extra you should
add to your program
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 27:
Recursion
Brain Exercise
Brain teaser: Linda is a 31-year-old woman,
single and bright. When she was a student she
was deeply involved in social justice issue
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 23:
Generics
ArrayList
Homework
The StringSet is related to a mathematical set.
red shows the result of the operation
A
Intersection
AB
B
A
Union
AB
B
What is t
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 24:
Inheritance
Brain Exercise
What value should be in the blank?
2
40
10
81
35
0
2
1
2
0
98
Homework
You should strive to completely
understand all details of
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 29:
Intro to GUIs
Brain Game
Question: You have two sticks and a box
of matches.
Each stick takes exactly an hour to burn
from one end to the other. The sticks
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 25:
Abstract Methods
Interfaces
Implements
Brain Exercise
Find x
No trig
Homework
I am going to release homework on
Monday
due the week of the test
there wil
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 26:
Polymorphism
Assignment
Released this afternoon
due next Monday night
practice with generics, abstract super class
Polymorphism
From the Greek
poly means
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 32:
GUI Layouts
Making an Application
2D Arrays
We are used to 1D Java arrays
int[] vals = new int[5];
What if we want an array of arrays?
int[][] grid = new in
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 21:
Final and static
A Dynamic Array
A4
Scores
Median 94
Average 81
Pulled down by many 0s
Some fun animations!
Homework Questions
For testing
You can use
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 22:
A Dynamic Array
Generics
Dynamic Array
Contains a reference to an array
that variable can reference different arrays
change, add, and remove elements
Testi
CS1410: Introduction to
Object-Oriented Programming
Instructor: Dr. David E. Johnson
Lecture 28:
Practical Recursion
Brain Game
Play A Game: 2 people play.
1st person says a number from 1-10.
2nd person says a number adding 1 to 10 to that
1st person says
3 Chernoff-Hoeffding Inequality
When dealing with modern big data sets, a very common theme is reducing the set through a random
process. These generally work by making many simple estimates of the full data set, and then judging
them as a whole. Perhaps
4 Min Hashing
Last time we saw how to convert documents into sets. Then we discussed how to compare sets, specifically
using the Jaccard similarity. Specifically, for two sets A = cfw_0, 1, 2, 5, 6 and B = cfw_0, 2, 3, 5, 7, 9. The
Jaccard similarity is d
Data Mining
CS 5140 / CS 6140
Jeff M. Phillips
January 11, 2016
Data Mining
What is Data Mining?
I
Finding structure in data?
I
Machine learning on large data?
I
Unsupervised learning?
I
Large scale computational statistics?
Data Mining
What is Data Minin
8 Hierarchical Clustering
This marks the beginning of the clustering section. The basic idea is to take a set X of items and somehow
partition X into subsets, so each subset has similar items. Obviously, it would be great if we could be more
specific, but
2 Statistical Principles
We will study three phenomenon of random processes that are quite important, but possibly unintuitive. The
goal will be to explore, formalize, and hopefully make intuitive these phenomenon.
Birthday Paradox: To measure the expect
17 Compressed Sensing and
Orthogonal Matching Pursuit
Again we will consider high-dimensional data P . Now we will consider the uses and effects of randomness.
We will use it to simplify P (put it in a lower dimensional space) and to recover data after ra
7 Distances
We have mainly been focusing on similarities so far, since it is easiest to explain locality sensitive hashing
that way, and in particular the Jaccard similarity is easy to define in regards to the k-shingles of text documents. In this lecture
CS 3130 / ECE 3530: Probability and Statistics for Engineers
Extra Problems: Bayes Rule and Random Variables
1. Define events G = defendant is guilty and F = fingerprints match.
1
. Using the
From the question we know P (F |Gc ) = 0.01, P (F |G) = 1, and
CS 3130/ECE 35350: Probability and Statistics for Engineers
Homework 2: Total Probability, Independence, and Bayes
Rule
This solution set is for students enrolled in CS 3130 / ECE 3530 in Fall 2015 only.
Do not distribute to anyone else, doing so will be